L. Spector and E. Goodman and A. Wu and W. B. Langdon and H.-M. Voigt and M. Gen and S. Sen and M. Dorigo and S. Pezeshk and M. Garzon and E. Burke
editors,
Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO-2001
held in San Francisco, 7-11th July 2001,
(BibTeX)Amazon
Papers.

Riccardo Poli,W. B. Langdon,
Marc Schoenauer,
Terry Fogarty,
and
Wolfgang Banzhaf,
Late Breaking Papers at
EuroGP'98:
the First European Workshop on Genetic Programming.
This booklet is
available as technical report
CSRP-98-10
(33MB
you may want to save PDF to disk and then read it back.)
from the
School of Computer Science, The University of
Birmingham, Edgbaston, Birmingham, B15 2TT, UK.

Improving CUDA DNA Analysis Software with Genetic Programming,
William B. Langdon and Brian Yee Hong Lam and Justyna Petke and Mark Harman,
in GECCO 2015,
pp1063-1070.
DOIPDFSlidesFTP kit
Download
barracuda_0.7.107
Genetically Improved BarraCUDA,
W. B. Langdon and Brian Yee Hong Lam,
Technical Report
RN/15/03.
arXiv:1505.07855Replaced by article
in BioData Mining.seqanswers.com BarraCUDA
BarraCUDA is a C program which uses the BWA algorithm in parallel with
nVidia CUDA to align short next generation DNA sequences against a
reference genome. The genetically improved (GI) code is up to three
times faster on short paired end reads from
The 1000 Genomes Project
and 60% more accurate on a short BioPlanet.com
GCAT alignment benchmark.
GPGPU Barracuda running on a single K80 Tesla GPU can align
short paired end nextgen sequences up to ten times faster than bwa on
a 12 core CPU.

Importance of nodes within protein prediction trees.
Largest protein prediction tree.
The 125 (15%)
subtrees which change more than 10 training cases are highlighted in
black.
Of the remaining 725,
277 have no impact on fitness at all,
while a further 151 affect only one (of 1213) training case.
Note several large repeated subtrees
(which must produce the same values)
make little contribution to fitness.

Comparison of AdaBoost and Genetic Programming for combining Neural Networks for Drug Discovery,
W. B. Langdon and S. J. Barrett and B. F. Buxton.
Presented at
EvoBIO'2003,
11-14 April 2003,
LNCS 2611,
Essex,
p87-98, Springer-Verlag.
ps.gzDOI
With the help of a Publication Support Grant
from
Evolsolve.

Joint (co-authored) papers

Genetic Improvement of Software: a Comprehensive Survey,
Justyna Petke and Saemundur O. Haraldsson and Mark Harman and William B. Langdon and David R. White and John R. Woodward,
IEEE Transactions on Evolutionary Computation.
In press.
DOI

Using Genetic Improvement and Code Transplants to Specialise a C++ Program to a Problem Class,
Justyna Petke and Mark Harman and William B. Langdon and Westley Weimer,
In
EuroGP-2014,
Miguel Nicolau and Krzysztof Krawiec and Malcolm Heywood
eds.,
LNCS 8599, pp137-149, Springer.
PDFDOI.
Replaced by TSE articleWinner of Silver at GECCO 2014
Humieslides

Riccardo Poli
and W. B. Langdon
On the Search Properties of Different Crossover
Operators in Genetic Programming.
Presented at
GP-98
(GP-98
paper).
Cf.
CSRP-98-7
On the Ability to Search the Space of Programs of Standard,
One-point and Uniform Crossover in Genetic Programming